conda install -c pyviz hvplot
!pip install hvplot
!pip install numpy
!pip install <package>
!pip install Bokeh
!pip install Bokeh
pip install xlrd
!pip install descartes
pip install folium
!pip install plotly --upgrade
!pip install cufflinks --upgrade
!pip install geopandas
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import IFrame
IFrame("Kestrel.jpg", width=560, height=315)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import IFrame
IFrame("kestrel-png-kestrel-248.png", width=560, height=315)
from IPython.display import IFrame
IFrame("owl_PNG47.png", width=560, height=350)
A Technical REPORT ON WILDLIFE BIRDS In The U.K and the impact's from environmental to criminal issues that have affected them
Name:Luca Aiello
df = pd.read_csv('farmlandbirds.csv')
importing data
df.head()
df = pd.read_csv('woodlandbirds.csv')
df = pd.read_csv('seabirds.csv')
df = pd.read_csv('wetlandbirds.csv')
import glob
from glob import glob
bird_files = sorted(glob('*birds*.csv'))
bird_files
all_bird_files = pd.concat((pd.read_csv(file).assign(filename=file) for file in bird_files), ignore_index=True)
sort=True
sort=True
all_bird_files.shape
df.head()
df.describe(include = 'all')
df.dropna
df.count()
df.any()
df.all(axis=1)
df.corr(method='spearman')
df.cov()
df.sum()
df.groupby('Short term change (2012-2017)')
df.Species.head()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
fig, ax = plt.subplots(figsize=(100,100))
sns.lineplot(x='Species', y='Short term change (2012-2017)', ax=ax, data=df).set_title('birds changes over set years')
fig, ax = plt.subplots(figsize=(100,100))
sns.lineplot(x='Species', y='Annual percentage change', ax=ax, data=df).set_title('birds changes over set years')
#importing Pandas
import pandas as pd
#importing plotly and cufflinks in offline mode
import cufflinks as cf
import plotly.offline
cf.go_offline()
cf.set_config_file(offline=False, world_readable=True)
df.iplot(kind="scatter", theme="white",x="Long term change (1975-2017)",y="Annual percentage change",
categories="Species")
df[['Short term change (2012-2017)', 'Species']].pivot(columns='Short term change (2012-2017)', values='Species').iplot(kind='box')
df.loc[df['Annual percentage change'] != 'Annual percentage change.1'].tail().mean()
df.std(axis = 0, skipna = True)
df.head()
df.dropna()
df.loc[df['Annual percentage change'] == df['Annual percentage change'].max()]
df.loc[df['Annual percentage change'] == df['Annual percentage change'].min()]
df.plot(kind='bar', x='Long term change (1975-2017)', y='Annual percentage change')
df.plot(kind='bar', x='Short term change (2012-2017)', y='Annual percentage change.2')
df1 = df
df1_popTI = df1.pop('Long term change (1975-2017)')
df1_popTI
df.loc[df['Trend.1'] == 'Trend.1']
df2 = df
df2_popTI = df2.pop('Trend.1')
df2_popTI
df3 = df
df3_popTI = df3.pop('Annual percentage change')
df3_popTI
df.head()
df['Species'].unique()
df_Species_ = df[(df['Species'] == 'Common Sandpiper (Actitis hypoleucos)') | (df['Species'] == 'Dipper (Cinclus cinclus)') | (df['Species'] == 'Goosander (Mergus merganser)') | (df['Species'] == 'Grey Wagtail (Motacilla cinerea)') |(df['Species'] == 'Reed Bunting (Emberiza schoeniclus)')|(df['Species'] == 'Sedge Warbler (Acrocephalus schoenobaenus)')|(df['Species'] == 'Coot (Fulica atra)')|(df['Species'] == 'Great Crested Grebe (Podiceps cristatus)')|(df['Species'] == 'Little Grebe (Tachybaptus ruficollis)')|(df['Species'] == 'Mallard (Anas platyrhynchos)') |(df['Species'] == 'Moorhen (Gallinula chloropus)') |(df['Species'] == 'Tufted Duck (Aythya fuligula)') |(df['Species'] == 'Curlew (Numenius arquata)')|(df['Species'] == 'Lapwing (Vanellus vanellus)') |(df['Species'] == 'Little Egret (Egretta garzetta)') |(df['Species'] == 'Mute Swan (Cygnus olor)') |(df['Species'] == 'Redshank (Tringa totanus)') |(df['Species'] == 'Snipe (Gallinago gallinago)') |(df['Species'] == 'Teal (Anas crecca)') |(df['Species'] == 'Yellow Wagtail (Motacilla flava)') |(df['Species'] == 'Grey Heron (Ardea cinerea)')|(df['Species'] == 'Kingfisher (Alcedo atthis)')|(df['Species'] == 'Oystercatcher (Haematopus ostralegus)')|(df['Species'] == 'Sand Martin (Riparia riparia)')]
df_Species_.shape
sns.barplot(x='Species', y='Short term change (2012-2017)', data=df,).set_title ("change of bird species 2012-2017")
sns.despine()
sns.set(rc={'figure.figsize':(100.100,100.100)})
ax = sns.countplot(x="Species", data=df)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
ax.set_xticklabels(ax.get_xticklabels(), fontsize=10)
plt.show()
sns.lmplot('Annual percentage change.2', 'Short term change (2012-2017)', data=df, fit_reg=False, height=3,aspect=2)
sns.despine()
sns.set(rc={'figure.figsize':(.20,20.20)})
ax = sns.lmplot(x="Annual percentage change.2", data=df)
ax = sns.lmplot(y="Short term change (2012-2017)", data=df)
plt.show()
sns.jointplot(x= 'Short term change (2012-2017)', y= 'Annual percentage change.2', data=df, height=5)
df['Trend.2'].unique()
df_Trend_2 = df[(df['Trend.2'] == 'little change') | (df['Trend.2'] == 'weak increase') | (df['Trend.2'] == 'strong increase') | (df['Trend.2'] == 'weak decline') | (df['Trend.2'] == 'strong decline')]
df_Trend_2['Trend.2'].unique()
df_Trend_2.shape
df.groupby('Species')[['Trend.2']].sum()
fig, ax = plt.subplots(figsize=(20,8))
sns.countplot( x='Trend.2', data=df, ax=ax).set_title('Types of trends')
sns.pairplot(df, height=5)
df = pd.read_csv('prosecutionfalcos.csv')
df.head()
df.dropna
df.drop(['App.', 'Class', 'Origin', 'Unit', 'Purpose', 'Source', 'Class', 'Order'], axis=1, inplace=True)
df.head()
df.fillna(0)
df.describe()
df.count()
df.any()
df.any(axis=1)
df.rank(method='max')
df.rank(method='min')
df.groupby
df.dtypes
df.Importer.value_counts()
Japan United State of America and the United Arab Emirates amongst the highest
df.corr('pearson')
df['Family'].unique()
df_Family_ = df[(df['Family'] == 'Anatidae') | (df['Family'] == 'Falconidae') | (df['Family'] == 'Phasianidae') | (df['Family'] == 'Psittacidae') |(df['Family'] == 'Scolopacidae') |(df['Family'] == 'Struthionidae')|(df['Family'] == 'Trochilidae')|(df['Family'] == 'Accipitridae')|(df['Family'] == 'Bucerotidae')|(df['Family'] == 'Cacatuidae')|(df['Family'] == 'Rheidae')|(df['Family'] == 'Strigidae')|(df['Family'] == 'Theraphosidae')]
df_Family_ = df[(df['Family'] == 'Anatidae') | (df['Family'] == 'Falconidae') | (df['Family'] == 'Phasianidae') | (df['Family'] == 'Psittacidae') |(df['Family'] == 'Scolopacidae') |(df['Family'] == 'Struthionidae')|(df['Family'] == 'Trochilidae')|(df['Family'] == 'Accipitridae')|(df['Family'] == 'Bucerotidae')|(df['Family'] == 'Cacatuidae')|(df['Family'] == 'Rheidae')|(df['Family'] == 'Strigidae')|(df['Family'] == 'Theraphosidae')]
df_Family_.shape
df.groupby('Family')[['Exporter']].sum()
fig, ax = plt.subplots(figsize=(20,8))
sns.countplot( x='Family', data=df, ax=ax).set_title('Exporter biggest family of birds')
df = pd.read_csv('Confirmedincidents.csv')
df.head()
dfBirds_popTI = df.pop('OBJECTID'), ('RSPB Reference'), ('Grid_Square'), ('More_Info_Here'), ('GlobalID'), ('Shape__Area'), ('Shape__Length')
dfBirds_popTI
df_popTI = df.pop('RSPB_Reference')
df_popTI
df_popTI = df.pop('Grid_Square')
df_popTI
df_popTI = df.pop('More_Info_Here')
df_popTI
df_popTI = df.pop('GlobalID')
df_popTI
df_popTI = df.pop ('Shape__Length')
df_popTI
df_popTI = df.pop('Shape__Area')
df_popTI
df.head()
sns.factorplot(x="County", hue="Incident_Type",
... data=df, kind="count",
... height=20, aspect=20);
sns.factorplot(x="Country", hue="Incident_Type",
... data=df, kind="count",
... height=10, aspect=20);
sns.catplot(y="Incident_Type", kind="count",
palette="pastel", edgecolor=".6",
data=df, order = df['Incident_Type'].value_counts().index);
sns.catplot(y="Country", kind="count",
palette="pastel", edgecolor=".6",
data=df, order = df['Country'].value_counts().index);
import pandas as pd
import hvplot.pandas
import hvplot
import hvplot.pandas
import panel as pn
import hvplot.pandas
df_2007_18 = pd.read_csv('Confirmed_Raptor_Persecution_Incidents_Public.csv')
df_2007_18.head()
pd_join = pd.concat([df_2007_18])
pd_join.tail(3)
df_join_gp = pd_join.groupby(['Incident_Type', 'Year', 'Month']).size().to_frame(name='Count').reset_index()
df_join_gp.head()
df_join_gp_set_index = df_join_gp.set_index(['Month', 'Incident_Type',])
df_join_gp_set_index
df_join_gp_set_index.hvplot.bar(stacked=True, width=900, height=500, legend='left', rot=45)
df_join_gp_set_index.hvplot.bar(stacked=False, width=900, height=600, legend='top_left', rot=90)
df = pd.read_excel('Confirmed_Raptor_Persecution_Incidents_Public (1).xlsx')
df.head()
df.append(pd.DataFrame(df.sum(), index = ["Total"], columns=["County"]))
sort=False
df.append(pd.DataFrame(df.sum(), index = ["Total"], columns=["County"]))
sort=False
df.groupby(['County']).sum()
df.groupby('County')
df["County"].value_counts(dropna=False)
North Yorkshire is the highest and then the Highlands in Scotland
import pandas as pd
with open('out.csv', 'w') as out:
for col in df.columns[1:]:
res = df[col].value_counts()\
.reset_index()\
.rename(columns={col: 'count', 'index': col})\
res.to_csv(out, index=False)
creating a totals dataframe from the workbook into excel to read later
import folium
import pandas as pd
import json
import os
import requests
import numpy as np
import geopandas as gpd
import matplotlib.pyplot as plt
%matplotlib inline
boundaries =('Confirmed_Raptor_Persecution_Incidents_Public (4).geojson')
map_df = gpd.read_file(boundaries)
map_df.head(2)
df = pd.read_excel('out.xlsx')
df.head()
merged = df.set_index("County", "Total").join(map_df.set_index("County", "Total"))
merged = merged[merged['Total'].notnull()]
merged.head()
variable = 'Total'
vmin, vmax = 1,132
fig, ax = plt.subplots(1, figsize=(12, 8))
ax.axis("off")
plt.title('Birds persecutions 2007-2018')
merged.plot(variable, cmap='Blues', linewidth=0.4, markeredgecolor=0.4, ax=ax)
sm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm._A = []
cbar = fig.colorbar(sm)
merged = merged.reset_index()
map_df.plot()
m = folium.Map([52.6333, -1.1333], zoom_start=7, tiles="Stamen Terrain", control_scale=True)
choropleth = folium.Choropleth(
geo_data=boundaries,
data=merged,
columns=['Incident_Type', 'Total'],
key_on='feature.properties.County',
threshold_scale=[0, 5, 10, 15, 50, 135],
fill_color='PuBu', #options BuGn, BuPu, GnBu, OrRd, PuBu, PuBuGn, PuRd, RdPu, YlGn, YlGnBu, YlOrBr, and YlOrRd
bins=6,
legend_name='Number of birds persecuted 2007-2018',
).add_to(m)
choropleth.geojson.add_child(
folium.features.GeoJsonTooltip(['Incident_Type', 'County', 'Month'])
)
m
Contents Contents 1 Table of figures 1
- What exactly is a bird? 2 1.1 Guidelines set out from relevant stakeholders 2 1.2 Acts of parliament 2
- The Royal Society for Protection of Birds (RSPB) 3 2.1 The bird situation from an environmental point of view 3 2.3 The Migration of birds and the global environment 4
- Wildlife crime and birds 6 3.1 Problems facing convictions 7
- Methods for solutions criminological perpespectives 8 8 Problem Analysis Triangle (PAT) 8 Reference list 10
Table of figures Figure 1. Taken from become a problem solving analyst in 55 small steps (2003). 8
What exactly is a bird? The dictionary describes a bird as- ‘warn-blooded, egg-laying vertebrate distinguishable by possessing feathers, wings, beaks and typically being able to fly’.
‘The class known as Aves: are periodically categorized by being evolved from the Jurassic period from small dinosaurs where they too had some similar features, were warm blooded and relatively small in size’. (Lexico, 2020).
1.1 Guidelines set out from relevant stakeholders
Legality in the United Kingdom (UK) to use and hunt birds in described in the Government website GOV.UK (2020). This is a by no means absolute, more of a guideline especially when if the land belongs to the owner and the bird type is possibly causing agricultural and farm animal complication’s.
However, the constrictions exist for shooting and hunting of birds. Should only be done within season for game, waterfowl and quarry birds. For specific dates see (https://basc.org.uk/advice/quarry-species-shooting-seasons/). The government website also has a spectrum of the type of firearms, ammunition, that cannot be utilised when hunting and shooting birds such as ‘no more than 2 rounds of ammunition’. To hunt with a falcon, a falconry licence must be obtained as well. Furthermore, it specifies the ethical ways to hunt and shoot birds to aid the potential hunter to manage their integrity whilst carrying out these types of activities for the purposes of rules of engagement, to manage land, food consumption or for sports purposes.
1.2 Acts of parliament
Specifically, the Wildlife and Countryside Act.c.69 Part 1 (1981) ‘(1) Subject to the provisions of this Part, if any person intentionally— (a)kills, injures or takes any wild bird; (aa)takes, damages or destroys the nest of a wild bird included in Schedule (b)takes, damages or destroys the nest of any wild bird while that nest is in use or being built; or (c)takes or destroys an egg of any wild bird, he shall be guilty of an offence. (2)Subject to the provisions of this Part, if any person has in his possession or control— (a)any live or dead wild bird or any part of, or anything derived from, such a bird; or (b)an egg of a wild bird or any part of such an egg, he shall be guilty of an offence.
The conviction depending on severity is, unlimited fine, imprisonment for up to six months or both.
The Royal Society for Protection of Birds (RSPB)
The RSPB, Our History (2020) was founded by Emily Williamson in 1889 for the destruction of the use of feathers for the new fashionable hats in the Victorian era and moved to destructs the fur coat fashion at the time. Over many years her society grew and purchased land for conservation and passed new acts of parliament protecting birds (of which we still have today) but amended. Present day: RSPB has thousands of members of which contribute to the society enabling, the purchasing of land including greater London/Essex, places in Ireland, that which includes farmlands and areas in the Isle of Wight. Protecting certain species and creating wildlife projects to flourish habitats for Aves. Her legacy has continued to push for more wildlife conservationism that has even expanded to UK bases abroad for example, Cyprus.
2.1 The bird situation from an environmental point of view
The decline of birds as been gradual over the past 30 years and more recently in past 10 years as the RSPB describes Is the number of birds in decline? (2020). The decline has been seen more in farmland birds such as the Skylark and now, followed by woodland birds such as the Blue Tit. This is because, newer agricultural practices to increase efficiency, crop sowing and specialisation farming, that has stripped land back and decreased the natural food/water resources and nesting for birds. The same can be said for the agricultural practices throughout Europe. The Common Agricultural Policy (2003) is a step in the right direction in combating the massive decline are European agricultural practices as birds’ migrations takes place globally.
It has been suggested to the agricultural society that a multifunctional objectives need to be implemented Bradbury, R. B and Kirby, W. B. (2006) to create wet habits for birds for probing species and for their natural food and water resources to prevent catastrophic decline to resulting in extinctions. The decline can be reversed though, by such an implementation as they are overlooked in farming industry. This is also to diffuse pollution by water run of and soil compaction and chemical pesticides and herbicides creating severe floods and the quality of water is furthered diminished for the biodiversity of birds, and the introduction of non-native species with urbanisation making predatory birds less predatory.
Land sparing is another strategy that has and need to be more implemented as an approach to conservation for biodiversity that without it birds will indefinitely decline. This is because, birds are an indicator of a flourishing environment and land sparing as potential to populate species status. A study conducted by Lamb, A et al. (2019) for the United Kingdom forecast the predictions that land sparing can increase woodland and wetland bird’s species populations but, will have a slight decrease for farmland birds. This decline can be counteracted by good farming practices as suggested and, to be used in Europe to keep species from declining resulting from the migration seasons.
2.3 The Migration of birds and the global environment
Meaning of Weather (2020): ‘’the state of the atmosphere with respect to wind, temperature, cloudiness, moisture, pressure, etc’’.
Basic meaning of climate change by the Environmental Protection Agency: ‘any significant change in the measures of climate lasting for an extended period of time” and global warming as “the recent and ongoing rise in global average temperature near Earth's surface…caused mostly by increasing concentrations of greenhouse gases in the atmosphere, which trap infrared heat causing a warming effect’’ Critically describing climate change as; The warming of the earth by industrialisation which in turn can cause rises in sea levels and ironically droughts or extreme weather such as Tsunami exacerbated by the industrial revolution and the production of greenhouse gases. The damaging effects of climate change are amplified in a rising population and the extraction natural resources.
Certain bird species migrate and travel to many places across the globe during summer or winter (Reed, O. 2018) to Scandinavia, Eastern Europe also Africa (National Geographic, 2018). The reasons for this can vary. Such as for weather, food, water resources, nest and lay eggs (Which birds migrate? 2020). However, the degree of global warming, wars (terrorism) decimating countries urban and suburban stripping it’s environments, plastic pollutions and localised weather extreme fluctuations have contributed to perilous journeys to and from the UK with consequences of eradicating whole species.
Plastic pollution especially in the ocean is having huge affect in seabirds species decline, 32 billion tonnes of plastic is reported by 2016 (SAS,2020). As plastic enters a seabird food source-fish so does it enter the seabird in particular and thus slowly poisons the bird from the toxicity.
Sewage that has to travel from stream to river into the sea pollutes water for the bird to drink and that of its food source algae and fish. In 1990 it was reported that 27% of water was safe for bathing in the sea in the UK compared to now which is 96% (SAG, 2020). Furthermore, it was in past decades that sewage overflow created real problems.
More recently phenomena like locust plague in Africa have decimated crops for feeding (Stone, M, 2020) and migration to Australia by wetland birds (sandpiper) could further eradicate species for example the bush fires that sweeping through (Australia fires: a visual guide to bushfire crisis, 2020). Would likely cause early migration back to the UK or they may choose to head to a different country altogether which may upset the ecological balance of the new destination country for migration.
- Wildlife crime and birds
Wildlife crime is anything that contravenes current legislations e.g. Bird of Prey persecution – through poisoning, trapping, shooting, disturbance of nest and/or theft of chicks. Many police forces have dedicated special units to combat any such cases including the National Crime Agency, and other stakeholders such as the RSPCA (UK National Wildlife Crime Unit, 2020).
Organised criminals have specialised in stealing bird eggs and birds of prey such as owls (to ship they need to be kept warm and fed) and is increasing due to foreign interest where birds of prey hard to come by and owning one is seen as a status symbol, can be sold onto the black market for thousands of pounds and shipped to countries like Saudi Arabia to name a few.
A report of a man from Norfolk was convicted of stealing over five thousand bird eggs (Norfolk man illegally hoarded 5,000 rare eggs jailed, 2018). This caused baby birds in some sites a zero-survival rate this included, 298 species some rare birds meant that some species ‘’would not recover’’. However, this demonstrates how easy it can be to locate and, steal eggs. This was one man with a bird eggs hoarding addiction as he was not a seller. Here the legislation has be exercised, but the repercussions of it are low. However, if he was also selling on the black market the sentence would be much higher with the fine being unlimited one. He received a just an eighteen months sentence, the fine was not disclosed.
Operation Owl (2020) launched this 17th February governed by The National Police Chief Council, North Yorkshire Police, the Royal Society for the Protection of Birds and the RSPCA, together with the North York Moors and Yorkshire Dales National Parks. is a task force dedicated to finding those responsible in raptor related crimes. They concentrate on hot spots where known persecutions have occurred and aim to disrupt the offenders as birds like the peregrines (and a few others) are being targeted especially.
3.1 Problems facing convictions
Annual wildlife report as summarised the issues from crime scene to court, to gain convictions in wildlife crimes is as follows:
‘Examples are where police control rooms fail to recognise the complaint as a police matter, referring it to the RSPCA, or secondly where the complaint is accepted, officers often have no power or training to undertake investigations and basic procedural failures ensue, which include crime scenes not being examined to prove the badger sett was in current use or forensic evidence is lost. The continual pressure on police resources does not assist. The lack of available and rapid access to competent or expert witnesses is problematic. Court cases are often heavily contested by defence specialists, ranging from barristers to solicitors’, report on: Wildlife Crime (2017).
Methods for solutions criminological perspectives
Problem Analysis Triangle
from IPython.display import IFrame
IFrame("Triangle-for-problem-analysis (1).png", width=560, height=315)
Figure 1. Taken from become a Center for Problem- Oriented Policing (2003).
Crime= bird egg thefts,shooting ,trapping and posioning.
Offender= could be someonely posing as hicker or rural walker in order to have legigitmate and discrescionary means to being in the perticular areas
Place= Usually wildbirds location is rural as the geography (see heatmap above)
Target/Victim= the hunted bird and its eggs (if any) this could be one that is on the list as most vualable to catch and sell.
Handlers= This could be Police officers, signs and posters or news about bird crimes articles offenders read in and around close to crime hotspots. Applying harser fines and sentences if caught.
Managers= Ranger and their staff to maintain aware and find ways to deter offenders such as placing camera traps near a nest site.
Guardians= The bird stopping the theft of its eggs the bird itself if its of a prey type as most birds work in pairs when hunting and guarding their hunting area. Other hickers and nature walkers.
Rational choices determined by social, cultral, economical and emotional systems Gharajedaghi, J. (2011) will ultimetley deter or re-enforce this type of crime and as such environmental crimes and environmental problems, such as plastics, that’s likely causing birds to decrease further. This can be analysed by braking down three components: the monitoring, enforcement and prevention. In more by understanding how wildlife crime as a whole also, branches out directly with transnational organized crimes, terror groups, corruption, and other illicit markets, Moreto. D. W and Pires. F. S. (2018). Therefore having a spatial appraoch in combating specific crimes such as one linked to birds can aid the right logistical, stratigical and expert tools, specialist stakeholders and financial resources for combatting this crime, By Hook or by Crook a reference manual on illegal wildlife trade and prosecutions in the United Kingdom (1998).
Refrence List
BASC (2020) ‘Quarry species & shooting season’. The association for Shooting & Conservation. Available at: https://basc.org.uk/advice/quarry-species-shooting-seasons/ (Accessed: 4th March 2020).
BBC News (2020) ‘Australia fires: a visual guide to bushfire crisis’. Broadcasting House. Portland place: London, W1A 1AA. Available at: https://www.bbc.co.uk/news/world-australia-50951043 (Accessed:24th February 2020).
BBC News (2018) ‘Norfolk man illegally hoarded 5,000 rare eggs jailed’. Broadcasting House. Portland place: London, W1A 1AA. Available at: https://www.bbc.co.uk/news/uk-england-norfolk-46358627 (Accessed: 24th February 2020).
Bradbury, R. B and Kirby, W. B. (2006) ‘Farmland birds and resource protection in the UK: cross-cutting solutions for multi-functional farming’? Biological Conservation, 129 (4), pp. 530-542.
British Trust for Ornithology (BTO), Royal Society for the Protection of Birds (RSPB) (2019) released: 7th November 2019. ‘Statistical dataset NV07-wild bird population in the UK’. Biodiversity and Ecosystem Evidence, Department for Environment Food and Rural Affairs, Foss House, Kings Pool,1-2 Peasholme Green: York, YO1 7PX. Download available at: https://www.gov.uk/government/statistical-data-sets/env07-wild-bird-populations-in-the-uk (Accessed: 11th November 2019).
Cambridge Dictionary. (2020) ‘Weather noun’. Cambridge University Press. Available at: https://dictionary.cambridge.org/dictionary/english/weather (Accessed: 8th January 2020).
Scott, M. Clarke, V, R. and Newman, R, G. (2018) ‘The problem analysis triangle’, Center for Problem-Oriented Policing, University at Albany, New York. Available at: http://www.popcenter.org/about/?p=triangle (Accessed: 4th May 2020).
Holden, J. (1998) ‘Illegal wildlife trade in the United Kingdom-native species’. By Hook or by Crook a reference manual on illegal wildlife trade and prosecutions in the United Kingdom, The Royal Society for the Protection of Birds, pp.11-14.
GOV.UK. (2020) ‘Hunting and shooting wildlife’. Available at: https://www.gov.uk/hunting/Birds (Accessed: 21st February 2020).
Gharajedaghi, J. (2011) ‘System theory: the nature of the beast’. 3rd edn, in Pam Chester/Rachel Roumeliotis (eds.). System Thinking managing chaos and Complexity: A platform for Designing Business Architecture, London: Elsevier Science & Technology, pp. 29-54.
Gray, R. (2018) ‘The strange reason owl thefts may be on the rise’. BBC Worklife. Available at: https://www.bbc.com/worklife/article/20180626-why-owls-might-suffer-in-a-cashless-society (Accessed: 5th March 2020)
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